Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units

Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network...

Full description

Saved in:
Bibliographic Details
Published in:Journal of real-time image processing 2018-10, Vol.15 (3), p.631-642
Main Authors: Wang, Shui-Hua, Sun, Junding, Phillips, Preetha, Zhao, Guihu, Zhang, Yu-Dong
Format: Article
Language:English
Subjects:
Antennas
Artificial neural networks
Back propagation
C band
Classification
Computer Graphics
Computer Science
Graphics processing units
Image Processing and Computer Vision
Image segmentation
Methods
Multimedia Information Systems
Neural networks
Pattern Recognition
Polarimetry
Propagation
Radar imaging
Regression analysis
Remote sensing
Signal,Image and Speech Processing
Special Issue Paper
Support vector machines
Synthetic aperture radar
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network (CNN). L-band data over the San Francisco bay area and C-band data over Flevoland area were employed as the dataset. For the San Francisco bay PSAR image, our method achieved an overall accuracy of 97.32%, which was at least 2% superior to four state-of-the-art approaches. We provided the confusion matrix over test area, and the kernel visualization. We compared the max pooling and average pooling. We validated by experiment that four convolution layers perform the best. Besides, our method gave better results than AlexNet. The GPU yields a 173× acceleration on the training samples, and a 181× acceleration on the test samples, compared to standard CPU. For the Flevoland PSAR image, our 11-layer CNN also gives better overall accuracy than five state-of-the-art approaches. The convolutional neural network is better than traditional classifiers and is effective in remote sensing image segmentation.
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-017-0717-0